| 基于潜在类别-Logit模型的共享自动驾驶汽车使用意向 |
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| 引用本文： | 姚荣涵,龙梦,张文松,祁文彦.基于潜在类别-Logit模型的共享自动驾驶汽车使用意向[J].交通信息与安全,2022,40(2):135-144. |
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| 作者姓名： | 姚荣涵 龙梦 张文松 祁文彦 |
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| 作者单位： | 大连理工大学交通运输学院 辽宁 大连 116024 |
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| 基金项目： | 中央高校基本科研业务费项目;国家自然科学基金 |
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| 摘 要： | 自动驾驶技术和共享经济融合产生的共享自动驾驶汽车（SAV）可为人们提供优质的出行服务。为探究出行者选择SAV的行为特性，对受访者的社会经济属性、历史出行特性、行为态度特征进行调查，并采用正交试验设计出行方式选择意向调查问卷，收集到311份有效数据。为充分考虑个体异质性，利用潜在类别分析探究SAV使用者的潜在类别，并将所得潜在类别作为变量融入离散选择Logit模型，建立SAV使用意向的潜在类别-Logit模型。结合多项或混合Logit模型以及划分的3个潜在类别，根据4个合理的模型标定的性别、交通模式、SAV使用人群类型、等待时间等59个变量的参数，识别SAV使用意向的显著性影响因素，并采用7个拟合优度指标评价多项Logit、混合Logit、潜在类别-Logit等8个模型。利用边际效应分析，探讨出行方式属性对SAV使用意向的具体影响。结果表明:涉及3个潜在类别的离散选择Logit模型具有更强的解释性，这3个潜在类别可分别描述为冲动的积极创新者、矛盾的保守创新者和理智的保守使用者；不同潜在类别人群的显著性因素存在明显差异，SAV使用人群类型是不同潜在类别人群共有的显著性因素，其中SAV创新者在各个模型中的显著性水平值均小于0.1；潜在类别-Logit模型的第1类和第2类预测正确率比其他Logit模型分别高出5.9%~28.3% 和5.4%~18.5%，可以更好地解释出行者对SAV的使用意向；出行等待时间对出行者选择SAV的影响最大；当SAV选择概率接近于0.5时，轻微降低SAV人均出行费用最易引起选择私人小汽车的出行者转而选择SAV。
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| 关 键 词： | 智能交通 共享自动驾驶汽车 使用意向 潜在类别-Logit模型 |
| 收稿时间： | 2021-11-10 |
User Preferences for Shared Autonomous Vehicles Based on Latent-Class Logit Models |
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| YAO Ronghan,LONG Meng,ZHANG Wensong,QI Wenyan.User Preferences for Shared Autonomous Vehicles Based on Latent-Class Logit Models[J].Journal of Transport Information and Safety,2022,40(2):135-144. |
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| Authors: | YAO Ronghan LONG Meng ZHANG Wensong QI Wenyan |
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| Affiliation: | School of Transportation and Logistics, Dalian University of Technology, Dalian 116024, China |
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| Abstract: | Shared autonomous vehicles (SAV), which integrates autonomous driving and shared economy technology, provide people with high-quality travel services. Socio-economic attributes, historical travel characteristics, and behavioral attitude characteristics of the respondents are studied, and a questionnaire of stated preferences for travel mode choice is designed by an orthogonal experiment, then 311 valid data are collected to study their behavior characteristics for choosing SAV. A latent class analysis is carried out to fully consider individual heterogeneity and to explore latent classes of users. Integrating the latent classes as the variables into discrete choice Logit models, latent class-Logit models are formulated to study user's preference for SAV. By combining a multinomial or mixed Logit model with the three latent classes discovered, the significant influencing factors for SAV user preferences are recognized out of 59 variables, including gender, travel mode, SAV user group category, waiting time, etc., calibrated by four reasonable models. Moreover, seven indices of goodness of fit are measured to evaluate the effectiveness of eight models such as multinomial Logit, mixed Logit, and latent class-Logit. The marginal utility analysis is used to investigate the impacts of the attributes of travel mode on SAV preferences. Study results show that the discrete choice Logit models with three latent classes have a higher capacity for explaining the relationship between dependent and independent variables. The three classes can be described as the impulsive and positive innovator, contradictory and conservative innovator, and rational and conservative user respectively. It is also found that the significant influencing factors obviously vary across different latent class groups; the category of SAV users group is a significant factor for all latent class groups, and the significance level of SAV innovators in each model is less than 0.1;the accuracies of the first and second categories predicted by the latent class-Logit model are 5.9%~28.3% and 5.4%~18.5% higher than those predicted by other Logit models respectively.It is also found that waiting time has the greatest impact on travelers' choice of SAV; and when the probability of choosing SAV is close to 0.5, slightly reducing the travel cost of SAV is most effective to attract travelers to use SAV, rather than private cars for travel. |
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